Robot Textile and Fabric Inspection and Manipulation (RTFM)

Textile and fabric manipulation is an important area of robotics research that has applications both in the industry and in homes. Yet, advances in robotic manipulation of such deformable objects have lagged behind work on rigid objects due to the far more complex dynamics and configuration space. In this project we will apply novel, advanced deep-learning and sim-to-real transfer learning methods on a real-world problem of textile and fabric manipulation and inspection. We will advance the state of the art of perception/inspection and robotic manipulation of textile and fabric, in order to bridge the technological gap and enable automation of such material handling. We will demonstrate technological advances at technology readiness level TRL 4 – technology demonstrated in lab. The outcomes of this project will serve as foundation for future, applied implementations, which will increase the competitiveness of Slovenian and European companies that deal with production and logistics of textile and fabric items. The demand for automation and robotization of textile and fabric logistics processes is substantial, and expected to even considerably grow, because of the expansion of web-based e-commerce. Despite the economic allure, there is a barrier to uptake robots in applications that handle textile and fabric, because the state of technology in this field is not ready and requires continued basic research. Three key problems need to be solved to enable effective application of goal-directed robot manipulation of textile and fabric: 1) detection, characterization and inspection of textile objects, 2) goal directed manipulation of objects from one state to the other, and 3) planning a sequence of states that reach the goal state of such deformable object. Through the course of the project we will solve these problems. We will 1) develop a vision-based system that allows segmentation, characterization and inspection of the manipulated textiles/fabrics. It will be based on robust deep-learning-based multimodal segmentation and detection of key points relevant for grasping, as well as on unsupervised learning for defect detection. 2) We will develop and demonstrate effective goal directed handling and manipulation of textile/fabric objects. This will be achieved through learning of appropriate motion policies, where advanced deep learning and reinforcement learning methods in simulation and in the real world, sim-to-real methods and training of new, end-to-end vision-to-motion deep neural networks will be applied. 3) Finally, we will develop means to plan a sequence of actions based on a novel state representation of textile. It will be used to form graphs of states where each edge is a transition with information on the needed robot action. To demonstrate the technological advances, we will implement a bimanual robot cell for textile and fabric logistics at TRL4. It will detect, flatten, inspect and fold textiles and fabrics into desired goal states. The presented demonstration will cover all the major aspects of the project in perception/inspection and handling/manipulation of such deformable objects. Our consortium is composed of two research groups, renowned in their respective fields, with a history of research in this field and of successful cooperation. Together, we have the knowledge and the experience to advance the state of the art in the field of robotized textile and fabric inspection and manipulation.